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Treatment of intracranial and extracranial haemorrhages in a neonate with severe haemophilia B with recombinant factor IX infusion

2005· article· en· W2089460693 on OpenAlexaff
G. M. T. Guilcher, M.F. Scully, Michael Harvey, J. P. Hand

Bibliographic record

VenueHaemophilia · 2005
Typearticle
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsJaneway Children's Health and Rehabilitation CentreMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineHaemophiliaHaemophilia ARecombinant DNAHaemophilia BFactor IXContinuous infusionCoagulopathyPediatricsThrombosisAdverse effectAnesthesiaRecombinant factor VIIaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Intracranial (ICH) and extracranial (ECH) haemorrhages are potentially life-threatening events that may occur comorbidly in neonates with haemophilia. There is little data on the use of recombinant factor IX (rFIX; BeneFIX in the neonate. Children <15 years of age are known to require higher doses of recombinant Factor IX (FIX) than older persons, which raises specific concerns in the neonate due to the increased risk of thrombosis in this age group (Thromb Haemost 2002; 87: 431). This report describes a case in which a high rate of continuous infusion of recombinant FIX was used to treat a newborn with significant intracranial and subgaleal haemorrhages. A high rate of infusion maintained at 30-35 U kg(-1) h(-1) was necessary to maintain adequate FIX levels. Despite the high rate of continuous infusion, no adverse events were noted. Our patient had a rare genetic mutation causing severe haemophilia B. A neonate with severe haemophilia B was treated successfully with recombinant FIX through continuous infusion. A high rate of infusion was required and no complications were noted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.278
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2005
Admission routes1
Has abstractyes

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